User-adaptive statistical dialogue management using OpenDial

In Spoken Dialogue Systems, two techniques are currently used to create an optimal dialogue policy: hand-crafted rules and statistical procedures basing on machine learning. However, both types are not sufficient in complex areas where only limited training data is available. This thesis thus examines a hybrid approach to dialogue management that intents to combine the benefits of both rule-based and statistical methods. For this purpose, probabilistic rules are employed which depend on unknown parameters. Afterwards, these parameters are trained with supervised learning. Furthermore, the dialogue manager is designed to be adaptive to the user's cultural background and emotional condition as this is supposed to have a crucial influence on the conversational behaviour. The configuration is then investigated in the context of the KRISTINA domain. The conducted experiments reveal that it is possible to include emotional and cultural features in the dialogue management